scholarly journals Noah-MP-Crop: Introducing dynamic crop growth in the Noah-MP land surface model

2016 ◽  
Vol 121 (23) ◽  
pp. 13,953-13,972 ◽  
Author(s):  
Xing Liu ◽  
Fei Chen ◽  
Michael Barlage ◽  
Guangsheng Zhou ◽  
Dev Niyogi
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Crop Growth ◽  
Surface Model ◽  
Dynamic Crop Growth

scholarly journals Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation

2013 ◽  
Vol 10 (12) ◽  
pp. 8039-8066 ◽  
Author(s):  
Y. Song ◽  
A. K. Jain ◽  
G. F. McIsaac
Keyword(s):  
Latent Heat ◽  
Land Surface ◽  
Root Distribution ◽  
Carbon Allocation ◽  
Land Surface Model ◽  
Crop Growth ◽  
Surface Model ◽  
Energy Fluxes ◽  
Growth Processes ◽  
Dynamic Crop Growth

Abstract. Worldwide expansion of agriculture is impacting the earth's climate by altering carbon, water, and energy fluxes, but the climate in turn is impacting crop production. To study this two-way interaction and its impact on seasonal dynamics of carbon, water, and energy fluxes, we implemented dynamic crop growth processes into a land surface model, the Integrated Science Assessment Model (ISAM). In particular, we implemented crop-specific phenology schemes and dynamic carbon allocation schemes. These schemes account for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, root, stem, and grain pools. The dynamic vegetation structure simulation better captured the seasonal variability in leaf area index (LAI), canopy height, and root depth. We further implemented dynamic root distribution processes in soil layers, which better simulated the root response of soil water uptake and transpiration. Observational data for LAI, above- and belowground biomass, and carbon, water, and energy fluxes were compiled from two AmeriFlux sites, Mead, NE, and Bondville, IL, USA, to calibrate and evaluate the model performance. For the purposes of calibration and evaluation, we use a corn–soybean (C4–C3) rotation system over the period 2001–2004. The calibrated model was able to capture the diurnal and seasonal patterns of carbon assimilation and water and energy fluxes for the corn–soybean rotation system at these two sites. Specifically, the calculated gross primary production (GPP), net radiation fluxes at the top of the canopy, and latent heat fluxes compared well with observations. The largest bias in model results was in sensible heat flux (SH) for corn and soybean at both sites. The dynamic crop growth simulation better captured the seasonal variability in carbon and energy fluxes relative to the static simulation implemented in the original version of ISAM. Especially, with dynamic carbon allocation and root distribution processes, the model's simulated GPP and latent heat flux (LH) were in much better agreement with observational data than for the static root distribution simulation. Modeled latent heat based on dynamic growth processes increased by 12–27% during the growing season at both sites, leading to an improvement in modeled GPP by 13–61% compared to the estimates based on the original version of the ISAM.

scholarly journals A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 1: Model description

2016 ◽  
Vol 9 (11) ◽  
pp. 4133-4154 ◽  
Author(s):  
Yuji Masutomi ◽  
Keisuke Ono ◽  
Masayoshi Mano ◽  
Atsushi Maruyama ◽  
Akira Miyata
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Agricultural Land ◽  
Land Surface Model ◽  
Crop Growth ◽  
Paddy Rice ◽  
Model Description ◽  
Surface Model ◽  
Crop Growth Model ◽  
Combined Model

Abstract. Crop growth and agricultural management can affect climate at various spatial and temporal scales through the exchange of heat, water, and gases between land and atmosphere. Therefore, simulation of fluxes for heat, water, and gases from agricultural land is important for climate simulations. A land surface model (LSM) combined with a crop growth model (CGM), called an LSM-CGM combined model, is a useful tool for simulating these fluxes from agricultural land. Therefore, we developed a new LSM-CGM combined model for paddy rice fields, the MATCRO-Rice model. The main objective of this paper is to present the full description of MATCRO-Rice. The most important feature of MATCRO-Rice is that it can consistently simulate latent and sensible heat fluxes, net carbon uptake by crop, and crop yield by exchanging variables between the LSM and CGM. This feature enables us to apply the model to a wide range of integrated issues.

scholarly journals Validation of IVINE crop growth model using MACSUR2 project measurements in a few European vineyards.

2020 ◽  
Author(s):  
Claudio Cassardo ◽  
Valentina Andreoli ◽  
Federico Spanna
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Input Data ◽  
Grid Point ◽  
Land Surface Model ◽  
Crop Growth ◽  
Meteorological Conditions ◽  
Surface Model ◽  
Crop Growth Model ◽  
Environmental Forcing

<p>The numerical crop growth model IVINE (Italian Vineyard Integrated Numerical model for Estimating physiological values) was originally developed at the dept. of Physics, Univ. of Torino, as a research model with the aim to simulate grapevine phenological and physiological processes. Since vines are generally strongly sensitive to meteorological conditions, the model should be able to evaluate the environmental forcing effects on vine growth and, eventually, on its production. IVINE model requires a set of hourly meteorological and soil data as boundary conditions; the more relevant input for the model to correctly simulate the plant growth are: air temperature and soil moisture. Among the principal IVINE outputs, we mention: the main philological stages (dormancy exit, bud-break, fruit set, veraison, and harvest), the leaf development, the yield, the berry sugar concentration, and the predawn leaf water potential. The IVINE requires to set some experimental parameters depending on the cultivar; at present, IVINE is optimized for Nebbiolo and other common varieties (such as, for example, cvs. Barbera, Vermentino, Cannonau, etc for Italy), but validation experiments have been performed only for Nebbiolo variety, due to the difficulty to gather all required measurements useful to drive the model and to compare its outputs for several consecutive years in the same vineyard. In the frame of the second part of the EU JPI-FACCE project named MACSUR (Modelling European Agriculture with Climate Change for Food Security), some data relative to vineyards displaced in several European countries were made available, thus we tried to execute simulations with IVINE in those vineyards. Since input data required by IVINE were not all present, we decided to extract input data from the international GLDAS database in the nearest grid point to the experimental vineyard, and to run the trusted land surface model UTOPIA on those points in order to evaluate soil variables required by IVINE. The main results obtained by those simulations, as well as the few possible validations with experimental observations, will be shown and commented. As a summary, we can say that the simulation carried out with IVINE seems able to well account for the interannual variability of the meteorological conditions, and the used settings seems able to allow a sufficiently valid simulation of the pheno-physiological conditions of the vineyards, but the approximation in the input data causes departures larger than if local measurements would be used.</p>

scholarly journals A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice Ver. 1) – Part I: Model description

2016 ◽  
Author(s):  
Yuji Masutomi ◽  
Keisuke Ono ◽  
Masayoshi Mano ◽  
Atsushi Maruyama ◽  
Akira Miyata
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Agricultural Land ◽  
Land Surface Model ◽  
Crop Growth ◽  
Paddy Rice ◽  
Model Description ◽  
Surface Model ◽  
Crop Growth Model ◽  
Combined Model

Abstract. Crop growth and agricultural management can affect climate at various spatial and temporal scales through the exchange of heat, water, and gases between land and atmosphere. Therefore, accurate simulation of fluxes for heat, water, and gases from agricultural land is important for climate simulations. A land surface model (LSM) combined with a crop growth model (CGM), called LSM-CGM combined model, is a useful tool for simulating these fluxes from agricultural land. Therefore, we developed a new LSM-CGM combined model for paddy rice fields, the MATCRO-Rice model. The main objective of this paper is to present the full description of MATCRO-Rice. The most important feature of MATCRO-Rice is that it can consistently simulate latent and sensible heat fluxes, net carbon flux, and crop yield by exchanging variables between the LSM and CGM. This feature enables us to apply the model to a wide range of integrated issues.

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